Process control systems and methods having learning features

ABSTRACT

A system for operating a process includes a processing circuit that uses a self-optimizing control strategy to learn a steady-state relationship between an input and an output. The processing circuit is configured to switch from using the self-optimizing control strategy to using a different control strategy that operates based on the learned steady-state relationship.

CROSS-REFERENCE TO RELATED APPLICATIONS

This is a continuation-in-part of Ser. No. 12/777,097, filed May 10,2010, the entirety of which is hereby incorporated by reference.

BACKGROUND

Self-optimizing control strategies such as extremum seeking control canbe effective tools for seeking optimum operating conditions in a processcontrol system. Some loss (e.g., a hunting loss) and equipment wear,however, may be associated with any self-optimizing control strategythat uses a varying signal to conduct the search for optimum operatingconditions. It is challenging and difficult to develop robust processcontrol systems and methods.

SUMMARY

One embodiment of the invention relates to a system for operating aprocess. The system includes a processing circuit that uses aself-optimizing control strategy to learn a steady-state relationshipbetween a manipulated variable and an output variable. The processingcircuit is configured to switch from using the self-optimizing controlstrategy to using a second control strategy that operates based on thelearned steady-state relationship.

Another embodiment of the invention relates to a system for operating aprocess. The system includes a processing circuit. The processingcircuit includes at least one sensor input, an extremum seekingcontroller, and a model-based controller. The processing circuit isconfigured to switch between using the extremum seeking controller tocontrol the process and using the model-based controller to control theprocess. The processing circuit is configured to store processcharacteristics of a steady-state of the extremum seeking controller andthe processing circuit is configured to operate the model-basedcontroller using the stored process characteristics.

Another embodiment of the invention relates to a method for operating aprocess. The method includes using a self-optimizing control strategy tolearn a steady-state relationship between measured inputs and outputsthat minimizes energy consumption. The method further includes switchingfrom using the self-optimizing control strategy to using an open-loopcontrol strategy that operates based on the learned steady-staterelationship between measured inputs and outputs that minimizes energyconsumption.

Another embodiment of the invention relates to a method for operating aprocess. The method includes using a processing circuit to cause anextremum seeking controller to control the process. The method furtherincludes storing process characteristics of a steady-state of theextremum seeking controller in a memory device. The method yet furtherincludes switching from using the extremum seeking controller to controlthe process to using a model-based controller to control the process.The method also includes operating the model-based controller using thestored process characteristics.

Another embodiment of the invention relates to a method for operating aprocess. The method includes using a self-optimizing controller to learna steady state relationship between a manipulated variable and an outputvariable. The method also includes switching from using theself-optimizing controller to using a second controller that operatesbased on the learned steady state relationship. The second controlstrategy may be an open loop control strategy that conducts open loopcontrol based on control variables observed while the process wasoperating in the learned steady state relationship. The method mayfurther include operating the process using the second controllerprimarily and operating using the self-optimizing controllerperiodically. The method can also or alternatively include operating theprocess using the self-optimizing controller during at least one of astart-up state and a training state of the process. In some embodiments,the method can include detecting whether a steady state has beenobtained and learning the steady state relationship between amanipulated variable and an output variable by recording calculatedand/or sensed parameters existing during the steady state relationship,the calculated and/or sensed parameters provided to the secondcontroller for operation of a model-based control strategy. Theself-optimizing controller may be an extremum seeking controller.

Another embodiment relates to a system for controlling a cooling towerthat cools condenser fluid for a condenser of a chiller. The systemincludes a cooling tower fan system that controllably varies a speed ofat least one fan motor. The system further includes an extremum seekingcontroller that receives inputs of power expended by the cooling towerfan system and of power expended by the chiller. The extremum seekingcontroller provides an output to the cooling tower fan system thatcontrols the speed of the at least one fan motor. The extremum seekingcontroller determines the output by searching for a speed of the atleast one fan motor that minimizes the sum of the power expended by thecooling tower fan system and the power expended by the chiller. Thesystem further includes a model-based controller for controlling thespeed of the at least one fan motor. The system also includes aprocessing circuit configured to store process characteristicsassociated with a steady-state of the extremum seeking controller. Theprocessing circuit is further configured to switch from using theextremum seeking controller to using the model-based controller tocontrol the fan speed. The processing circuit operates the model-basedcontroller using the stored process characteristics. The processingcircuit may be configured to use the extremum seeking controller duringan initial training period. The stored process characteristicsassociated with the steady state of the extremum seeking controller andused by the model-based controller can include PLR_(twr,cap) (thepart-load ratio at which the tower operates at its capacity) and β_(twr)(the slope of the relative tower airflow versus the part-load ratio).The processing circuit may be configured to store the maximum part loadratio (PLR_(max)) during the training period and the minimum part loadratio (PLR_(min)) during the training period. The processing circuit maybe configured to monitor the part load ratio during the model-basedcontrol and wherein the processing circuit is configured to switch backto using the extremum seeking controller if the part load ratio exceedsPLR_(max) or drops below PLR_(min) during operation using themodel-based controller.

Alternative exemplary embodiments relate to other features andcombinations of features as may be generally recited in the claims.

BRIEF DESCRIPTION OF THE FIGURES

The disclosure will become more fully understood from the followingdetailed description, taken in conjunction with the accompanyingfigures, wherein like reference numerals refer to like elements, inwhich:

FIG. 1 is a block diagram of a system for operating a process, accordingto an exemplary embodiment;

FIG. 2 is a flow chart of a method for operating a process, according toan exemplary embodiment;

FIG. 3 is a detailed block diagram of a system for operating a process,according to an exemplary embodiment;

FIG. 4 is a detailed flow chart of a method for operating a process,according to an exemplary embodiment;

FIGS. 5A-5C relate to a particular implementation for one or morecontrol systems or processes described herein;

FIG. 5A is a depiction of a model for determining tower airflow as afunction of a chilled water load, according to an exemplary embodiment;

FIG. 5B is an illustration of a relationship between the cooling towerfan power and a corresponding chiller's power; and

FIG. 5C is a block diagram of an HVAC system, according to an exemplaryembodiment.

DETAILED DESCRIPTION

Before turning to the figures, which illustrate the exemplaryembodiments in detail, it should be understood that the disclosure isnot limited to the details or methodology set forth in the descriptionor illustrated in the figures. It should also be understood that theterminology is for the purpose of description only and should not beregarded as limiting.

Referring generally to the Figures, systems and methods are shown foroperating a process. A system includes a processing circuit that uses aself-optimizing control strategy to learn a steady-state relationshipbetween a manipulated variable and an output variable. The processingcircuit is configured to switch from using the self-optimizing controlstrategy to using a different control strategy that operates based onthe learned steady-state relationship.

Referring now to FIG. 1, a block diagram of a system for operating aprocess 102 is shown, according to an exemplary embodiment. Process 102may be any type of process that can be controlled via a processcontroller. For example, process 102 may be an air handling unitconfigured to control temperature within a building space. In otherembodiments, process 102 can be or include a chiller operation process,a damper adjustment process, a mechanical cooling process, a ventilationprocess, or any other process where a variable is manipulated to affecta process output or variable.

Process controller 104 operates process 102 by outputting andcontrollably changing a manipulated variable provided to process 102. Anoutput variable affected by process 102 or observed at process 102(e.g., via a sensor) is received at process controller 104. Processcontroller 104 includes logic that adjusts the manipulated variable toachieve a target outcome for process 102 (e.g., a target value for theoutput variable).

In some control modes, the logic utilized by process controller 104utilizes feedback of an output variable. The logic utilized by processcontroller 104 may also or alternatively vary the manipulated variablebased on a received input signal (e.g., a setpoint). The setpoint may bereceived from a user control (e.g., a thermostat), a supervisorycontroller, or another upstream device.

Process controller 104 is shown to include a self-optimizing controller106, a model-based controller 108, and a control strategy switchingmodule 110. Self-optimizing controller 106 may be configured to searchfor values of the manipulated variable that optimize the output variable(i.e., a controlled variable, a measured output variable, a calculatedoutput variable, etc.). In an exemplary embodiment, self-optimizingcontroller 106 is an extremum seeking control (ESC) module orcontroller.

Extremum seeking control is a class of self-optimizing control that candynamically search for the unknown and/or time varying input or inputsof a process to optimize a certain performance index (e.g., approach atarget value for one or more output variables). Extremum seeking controlcan be implemented using gradient searching through the use of ditheringsignals (e.g., sinusoidal, square-wave, etc.). That is, the gradient ofthe process's output (e.g., the output variable) with respect to theprocess's input (e.g., the manipulated variable) is typically obtainedby perturbing (e.g., varying in a controlled manner, oscillating, etc.)the manipulated variable and applying a corresponding demodulation onthe observed changes in the output variable. Improvement or optimizationof system performance is sought by driving the gradient toward zero byusing integration. Extremum seeking control is typically considered anon-model based control strategy, meaning that a model for thecontrolled process is typically not relied upon by the extremum seekingcontroller to optimize the system. While self-optimizing controller 106is preferably an extremum seeking controller, in some alternativeembodiments self-optimizing controller 106 can use other self-optimizingcontrol strategies. Some embodiments of self-optimizing controller 106may implement the extremum seeking control systems or methods describedin one or more of U.S. application Ser. No. 11/699,589, filed Jan. 30,2007, U.S. application Ser. No. 11/699,860, filed Jan. 30, 2007, U.S.application Ser. No. 12/323,293, filed Nov. 25, 2008, U.S. applicationSer. No. 12/683,883, filed Jan. 7, 2010, and U.S. application Ser. No.12/650,366, filed July 16.

Referring now to FIG. 2 in addition to FIG. 1, process controller 104uses self-optimizing controller 106 to learn a steady-state relationshipbetween a manipulated variable and an output variable (step 202 ofprocess 200). Process controller 104 switches from using aself-optimizing controller 106 to using a model-based controller 108that uses the learned steady-state relationship (step 204 of process200).

Learning a steady state relationship can include detecting a steadystate condition for process 102 and storing, for example, a manipulatedvariable that corresponds with a target output variable. In otherembodiments, learning a steady state relationship can be or includestoring a multiplier, a coefficient, a residual, or another variable orset of variables that describes a determined mathematical relationshipbetween a manipulated variable and an output variable. In yet anotherexample, a table of values around a steady-state operating point may beestablished for responding to varying input signals or varying processconditions. For example, where the process controller is configured toset an air handling unit damper position to cause a room temperature toapproach a setpoint input signal, the process controller 104 can store amatrix that relates a plurality of possible temperatures to damperpositions based on a learned steady state relationship. Accordingly,non-linear relationships between the manipulated variable and the outputvariable may be stored based on steady-state relationships between thetwo variables. In other embodiments, multiple coefficients of amulti-variable equation describing the relationship between themanipulated variable and the output variable may be determined andstored to describe non-linear steady-state relationships.

The decision to switch from using the self-optimizing controller 106 tousing the model-based controller 108 may be completed by controlstrategy switching module 110 or another logic module of processcontroller 104.

The model-based controller 108 may be a closed-loop controller, afeedback controller, a feedforward controller, an open-loop controller,or any other controller that uses one or more models to determinecontrol adjustments to the manipulated variable or variables.

Referring now to FIG. 3, a more detailed block diagram of a system 300for operating a process 302 is shown, according to an exemplaryembodiment. Process controller 304 includes a processing circuit 312that uses a self-optimizing controller 306 to learn a steady staterelationship between a manipulated variable and an output variable. Theprocessing circuit 312 is configured to switch from using theself-optimizing controller 306 to using a different control strategy(e.g., that of model-based controller 308) that operates based on thelearned steady-state relationship.

Process controller 304 is shown to include processing circuit 312.Processing circuit 312 is shown to include a processor 314 and a memory316. According to an exemplary embodiment, processor 314 and/or all orparts of processing circuit 312 can be implemented as a general purposeprocessor, an application specific integrated circuit (ASIC), one ormore programmable logic controllers (PLCs), one or more fieldprogrammable gate arrays (FPGAs), a group of processing components, oneor more digital signal processors, other suitable electronicscomponents, or a combination thereof.

Memory 316 (e.g., memory unit, memory device, storage device, etc.) isone or more devices for storing data and/or computer code for completingand/or facilitating the various processes described in the presentdisclosure. Memory 316 may be or include volatile memory or non-volatilememory. Memory 316 may include database components, object codecomponents, script components, or any other type of informationstructure for supporting the various activities described in the presentdisclosure. According to an exemplary embodiment, memory 316 iscommunicably connected to processor 314 via processing circuit 312 andincludes computer code for executing (e.g., by processor 314) one ormore processes described herein. Memory 316 may also include variousdata regarding the operation of one or more of the control loopsrelevant to the system (e.g., performance map data, historical data,behavior patterns regarding process behavior, state machine logic,start-up logic, steady-state logic, etc.).

Interfaces 324, 326, 328 may be or include any number of jacks, wireterminals, wire ports, wireless antennas, or other communicationsinterfaces for communicating information or control signals (e.g., acontrol signal of the manipulated variable output at interface 326,sensor information received at input interface 324, setpoint informationreceived at communications interface 328, etc.). Interfaces 324, 326 maybe the same type of devices or different types of devices. For example,input interface 324 may be configured to receive an analog feedbacksignal (e.g., an output variable, a measured signal, a sensor output, acontrolled variable) from a controlled process component (or a sensorthereof) while communications interface 328 may be configured to receivea digital setpoint signal from upstream supervisory controller 332 vianetwork 330. Output interface 326 may be a digital output (e.g., anoptical digital interface) configured to provide a digital controlsignal (e.g., a manipulated variable) to a controlled process component.In other embodiments, output interface 326 is configured to provide ananalog output signal. In some embodiments the interfaces can be joinedas one or two interfaces rather than three separate interfaces. Forexample, communications interface 328 and input interface 324 may becombined as one Ethernet interface configured to receive networkcommunications from a supervisory controller. In other words, thesupervisory controller may provide both the setpoint and processfeedback via an Ethernet network (e.g., network 330). In such anembodiment, output interface 326 may be specialized for the controlledprocess component of process 302. In yet other embodiments, outputinterface 326 can be another standardized communications interface forcommunicating data or control signals. Interfaces 324, 326, 328 caninclude communications electronics (e.g., receivers, transmitters,transceivers, modulators, demodulators, filters, communicationsprocessors, communication logic modules, buffers, decoders, encoders,encryptors, amplifiers, etc.) configured to provide or facilitate thecommunication of the signals described herein.

Memory 316 includes master control module 318, model-based controller308, self-optimizing controller 306, and control parameter storagemodule 322. Master control module 318 may generally be or includesoftware for configuring processing circuit 312 generally and processor314 particularly to operate process 302 using a self-optimizing controlstrategy (via self-optimizing controller 306) to learn a steady-staterelationship between a manipulated variable and an output variable. Oncethe relationship is learned or in response to one or more otherconditions (e.g., a time expiring), master control module 318 switchescontrol of operation of the process from the self-optimizing controlstrategy to a different control strategy (e.g., via model-basedcontroller 308). Master control module 318 causes the model-basedcontroller to operate based on the steady-state relationship learnedusing the self-optimizing controller.

As the self-optimizing controller 306 operates (e.g., seeking optimalvalues for the manipulated variable), the output variable (and, in someembodiments, any other inputs used by the self-optimizing controller)are provided to control parameter storage module 322. The manipulatedvariable output from the self-optimizing controller 306 is also providedto control parameter storage module 322.

In some embodiments, control parameter storage module 322 may beconfigured to store a detailed history of output variable to manipulatedvariable data sets. For example, control parameter storage module 322may be configured to store output variable to manipulated variable datapairs with a timestamp on an every-minute basis. In other embodiments,different intervals of control parameter recording may be effected bycontrol parameter storage module 322 (e.g., every second, every tenminutes, hourly, etc.). Control parameter storage module 322 may beconfigured to store data as it is received. In other embodiments,control parameter storage module 322 may be configured to smooth,average, aggregate, or otherwise transform the data for storage or use.For example, in one embodiment, control parameter module 322 may storean exponentially weighted moving average of output variables and anexponentially weighted moving average of manipulated variables. In someembodiments, other than conducting some basic transformation and storagerelative to output variables (or other system inputs) and themanipulated variable, the control parameter storage module 322 does notconduct significant additional processing. In other embodiments, controlparameter storage module 322 can further evaluate the received variablesor the stored information to build or identify a model for use by themodel-based controller 308. For example, the control parameter storagemodule 322 may be configured to describe the relationship between theoutput variable and the manipulated variable as a complex expression, asa system of coefficients for an equation, as a coefficient matrix, as asystem of rules, or as another model for describing the relationshipbetween the output variable(s) and the manipulated variable. Whencontrol of the process 302 is switched from the self-optimizingcontroller 306 to the model-based controller 308, the control parameterstorage module 322 provides stored parameters, coefficients, rules, orother model descriptors to model-based controller 308 so thatmodel-based controller 308 can operate the process 302 using therelationship learned by operation of the process 302 usingself-optimizing controller 306.

In the embodiment shown in FIG. 3, master control module 318 includes asteady state evaluator 320 and a control strategy switching module 310.Steady state evaluator 320 is configured to receive parameters fromcontrol parameter storage module 322. The steady state evaluator 320 candetermine whether the self-optimizing controller 306 has reached asteady state. Steady state evaluator 320 can evaluate a steady state byestablishing thresholds and checking for whether control parameters staywithin the thresholds for a period of time. In other embodiments, steadystate evaluator 320 can wait for a standard deviation of one or morestandard deviations of a control parameters to shrink below a certainvalue, can initiate a timer when the standard deviations first fallbelow the certain value, and can determine that a steady state conditionexists when the timer has elapsed a predetermined amount of time. Steadystate evaluator 320 can provide a result of its determination and/orother state describing information to control strategy switching module310. In an exemplary embodiment, control strategy switching module 310causes and coordinates the switch between self-optimizing controller 306and model-based controller 308. Control strategy switching module 310can wait a predetermined (or random, quasi-random) period of time aftersteady state evaluator 320 indicates a steady state to effect a switchfrom self-optimizing controller 306 to model-based controller 308.

Control strategy switching module 310 can cause the switch fromself-optimizing controller 306 to model-based controller 308 via aninstant or hard switch. For example, for a first time period the process302 may be entirely controlled by self-optimizing controller 306 and ina second time period the process 302 is entirely controlled bymodel-based controller 308. In another embodiment, the control strategyswitching module 310 may be configured to include one or more logicmechanisms for smoothing the switch from one controller to anothercontroller. In one such example, the control strategy switching module310 may restrict output from model-based controller 308 but may beginproviding inputs to model-based controller 308 some seconds or minutesearly.

In an alternative embodiment to that shown in FIG. 3, control strategyswitching module 310 and master control module 318 may be locateddownstream of model-based controller 308 and self-optimizing controller306. In such embodiments, control strategy switching module 310 may beconfigured to average, blend, or otherwise smooth the transition ofcontrol from one controller to the other controller.

In some embodiments, steady state evaluator 320 and control strategyswitching module 310 are configured to cause control to be switched backto self-optimizing controller 306 from model-based controller 308. Forexample, steady state evaluator 320 may be configured to receive thesame inputs that are being provided to model-based controller 308. Ifprocess 302 changes such that the inputs to model-based controller 308begin significantly changing, steady state evaluator 320 can communicatesuch a change to control strategy switching module 310. Control strategyswitching module 310, in response to such a communication, can thencause self-optimizing controller 306 to resume control of the process302 and for model-based controller 308 to discontinue control. Controlstrategy switching module 310 may then cause self-optimizing controller306 to operate until a new steady state is detected by steady stateevaluator 320. This cycle may operate continuously. In other words,control strategy switching module 310 can cause self-optimizingcontroller 306 to seek optimal manipulated variable to output variablerelationships until the process 302 is in or is brought to a steadystate. Once a steady state is detected and a control relationshipbetween the manipulated variable and the control variable is learned bythe self-optimizing controller 306 and stored in control parameterstorage module 322, the model-based controller 308 conducts control.When a condition is detected (e.g., a power outage, a restart, asignificantly different setpoint, an unstable process condition,deviation from steady state boundaries, etc.), the method repeats withself-optimizing controller 306 again conducting its seeking and learningbehaviors. During times when the model-based controller 308 is operatingprocess 302, the process components may advantageously be subjected toless energy loss and less equipment wear as compared to aself-optimizing controller that constantly oscillates the manipulatedvariable (and therefore process equipment) to seek optimal parameters.

While in some embodiments control strategy switching module 310 may onlyswitch back to operation using self-optimizing controller 306 when asteady state is no longer active, in other embodiments the controlstrategy switching module 310 may periodically cause control to beswitched back to the self-optimizing controller 306 from the model-basedcontroller 308. Operation by the self-optimizing controller 306 can beused to help determine whether a steady state still exists or can beused to determine whether performance of process 302 has changed.Operating self-optimizing controller 306 may allow control strategyswitching module 310 to determine that process 302 performance hasshifted or otherwise changed. In other words, the relationship that wasoriginally learned between a manipulated variable and one or more outputvariables may no longer be true or optimal. In yet other embodiments,periodic control by self-optimizing controller 306 can be used to detectfaults in the process 302. For example, if a newly detected relationshipbetween an optimal manipulated variable and the output variableindicates a significant change from a steady state or fault free stateknown to previously exist, the master control module 318 may cause afault alert and/or send related fault information to supervisorycontroller 332 via network 330. Such information may be used to displayfault information or alerts to a user via an electronic display or otheruser interface device. The user may then be able to check into andresolve the fault rather than allowing control to be learned relative toa faulty state. Advantageously, however, periodic learning provided byself-optimizing controller 306 may allow relatively optimal processsystem performance given the fault. In a system which operates only on afixed model, changed circumstances can result in an incorrect model andhighly undesirable results. A model learned by systems and methods ofthe present application can be optimal given even undesirablecircumstances.

Referring now to FIG. 4, a detailed flow chart of a method 400 foroperating a process system is shown, according to an exemplaryembodiment. Method 400 includes starting-up a controller (step 402).Starting-up of the controller may include one or more variableinitiation tasks, timer initiation tasks, feedback tasks, diagnosticstasks, or other control tasks. The start-up routine may include or befollowed by causing self-optimizing control operation of the controlledprocess system (step 404). According to varying exemplary embodiments,the self-optimizing control operation may be an extremum seeking controloperation. Method 400 includes checking whether start-up is complete(step 406). Checking whether start-up is complete can includedetermining whether a start-up timer has elapsed, checking for whether aset of post start-up conditions have been met, checking for whether astart-up routine has successfully completed, or otherwise. If start-upis completed, the method moves on to the next step. If start-up is notcomplete, the controller continues self-optimizing control operationuntil start-up is complete.

Method 400 further includes determining whether a steady state has beenattained by the self-optimizing control or the process system that theself-optimizing control is controlling (step 408). Determining whether asteady state has been attained by the self-optimizing control caninclude determining whether the manipulated variable is making steps orsinusoidal changes above a certain amplitude and/or frequency,determining whether the output variable is within a certain range,determining whether relationships identified during the self-optimizingcontrol fit a post start-up model, or conducting any other control ordecision task relevant in determining whether a steady state has beenattained. If the system has not reached a steady state, the controllercontinues self-optimizing control operation until a steady state isattained.

When step 408 results in a determination that a steady state has beenreached, the controller then records or updates one or more controlrelationships in memory (step 410). Recording or updating of controlrelationships can continuously occur when self-optimizing control isoperating the process system. For example, new manipulated variable tooutput variable relationships or values for describing the relationshipsmay be updated in memory for every regular time period of the processcontroller or process being controlled. Updating in memory may includereplacing previous variables, updating a moving average, or other tasksthat may help the controller more accurately or reliably describe arelationship observed during the self-optimizing control process. Insome embodiments, steps 404 through 410 can be considered a trainingperiod for the model-based control using a self-optimizing control loopas the training mechanism.

Method 400 further includes causing a switch to the model-based controlfor operation of the process system (step 412). The switch from theself-optimizing control to the model-based control may be as describedabove with reference to control strategy switching module 110 shown inFIG. 1, as described above with reference to control strategy switchingmodule 310 shown in FIG. 3, or completed by another control strategyswitching module or mechanism.

When the switch to the model-based control is effected, method 400 canstart or restart a periodic model update timer (step 414). The periodicmodel update timer is used later in the method to determine whether toswitch back to the self-optimizing control for a control model update.In varying alternative embodiments, method 400 may not utilize periodicmodel updating, step 414, or a periodic model update timer. In otherembodiments, the periodic model update timer may be user adjustable andset to zero to disable the feature. In an exemplary embodiment, theperiodic model update timer may initially be set for a relatively smallperiod of time (e.g., 30 minutes). If the model observed by theself-optimizing controller is determined to be relatively accurate infinding an accurate control model from self-optimizing control cycle toself-optimizing control cycle, the controller may be configured toautomatically begin lengthening the periodic model update timer (e.g.,in ten minute increments, in half-hour increments, in hour longincrements, etc.). For example, if the model is substantially unchangedfrom one self-optimizing control cycle to another, a periodic modelupdate timer that is initially set to 45 minutes may eventually allowthe model-based control to operate for four hour periods of time beforea “refresh” or update by the self-optimizing controller.

Model-based control using the recorded control relationship orrelationships continues or begins at step 416. As described above, themodel-based control can be feedforward-based, feedback-based, an openloop control strategy, or another model-based control strategy.

When the model-based control is operating, the method includesdetermining whether the periodic model update timer has elapsed (step418). If the periodic update time has elapsed, the controller loops backto step 404 and again causes self-optimizing control to operate theprocess system and to record or update control models or relationships(at step 410).

If the periodic model update timer has not elapsed, method 400determines whether operation of the model-based control strategy hasmoved out of one or more control boundaries (step 420). The controlboundary may be or include a threshold value for the output variable, athreshold value for the manipulated variable, one or more otherthresholds relating to a relationship between the manipulated variableand the output variable, one or more coefficients describing arelationship between the manipulated variable and the output variable, aperformance index parameter threshold, or another control boundarysuitable for determining whether the model operation has continuedwithin a desired range or bounds. If the model operation is not outsideof some boundary, then the model-based control using the recordedcontrol relationships continues at step 416.

When the model operation is determined to be outside of bounds at step420, the controller can then cause a switch to self-optimizing controloperation (step 422). After some period of time, the controller can thendetermine whether the self-optimizing control operation has reached asteady state (step 424). If the self-optimizing control operation hasnot reached a steady state, then the self-optimizing control continuesat step 422. When a steady state has been determined at step 424, method400 proceeds to determine whether the recorded relationships are stillvalid (step 426) (e.g., the relationships recorded at step 410).Determining whether the recorded relationships are still valid caninclude comparing coefficient values of an equation describing therelationships, comparing a sensor-obtained measurement to a setpoint, orconducting a number of other comparing, computing, or logic tasks.

If the recorded relationships are determined not to be valid, then thecontroller determines whether one or more system faults have occurred(step 428). Determining whether one or more system faults have occurredcan include further evaluation of self-optimizing control states orrelationships, performance indexes, measures sensor values, comparisonof one or more variables to one or more rules, or other analysis,computing, or determining tasks. If a system fault is detected at step428, then the system reports the fault to the user and requests userinput (step 430). Simple inspection or cleaning of the device mayresolve the fault or faults. If more complex fault analysis is required,the user may take the system offline and restart the controller andmethod at step 402. Due to the learning capabilities of the system, theself-optimizing control operation can record new relationship models forany new system performance realities (e.g., at step 410) prior tooperation by a model-based control strategy. If the controllerdetermines that there are not system faults that need to be reported toa user or otherwise addressed, the method loops back to step 404 andcauses or continues self-optimizing control operation such that controlrelationship models are recorded or updated at step 410.

If the recorded relationships are determined to be valid at step 426,the controller may then update model operation bounds (step 432). Step432 can include widening the bounds or otherwise making step 420 lessrestrictive. In an exemplary embodiment, the bounds can be narrowedafter step 428 (e.g., in situations where the model-operation wasdetermined to be within the established bounds but the relationshipswere found to be invalid). Once the bounds are updated (e.g., widened,made less restrictive, etc.) in step 432, method 400 loops back to step412, causing a switch to model-based control using the updated bounds.Adjusting the bounds to be more or less restrictive can advantageouslyadaptively the amount of time the system spends using self-optimizingcontrol versus model-based control (e.g., by widening bounds in responseto a determination that the model learned by the self-optimizing controlis still good, the system can learn to operate the model-based controlfor a longer period of time before resorting back to self-optimizingcontrol.

FIGS. 5A-5C relate to a particular implementation for one or morecontrol systems or processes described above, according to an exemplaryembodiment. Cooling towers are used to remove heat from chilled waterprovided to chiller condensers. Additional explanation and diagrams forexemplary cooling tower systems are contained in U.S. application Ser.No. 12/777,097. FIG. 5A depicts one model for determining tower airflowas a function of a chilled water load. Tower airflow may be computed asa linear function of the part load ratio (i.e., chilled water divided bydesign total chiller cooling capacity) with the following equation(“equation 1”):

G _(twr)=1−β_(twr)(PLR _(twr,cap) −PLR) for 1.0<PLR<0.25

whereG_(twr)=tower airflow divided by maximum airflow with all cellsoperating at high speedPLR=chilled-water load divided by design total chiller plant coolingcapacity (part-load ratio)PLR_(twr,cap)=part-load ration (value of PLR) at which tower operates atits capacity (G_(twr)=1)β_(wr)=slope of relative tower airflow (G_(twr)) versus part-load ratio(PLR)

When chiller operation is below 25% of the full load, the tower airflowis ramped to zero as the load goes to zero according to (“equation 2”):

G _(twr)=4PLR[1−β_(twr,cap)(PLR _(twr,cap)−0.25)] for PLR<0.25

The results of equation 1 or 2 are constrained between 0 and 1. Thefraction of tower capacity may then be converted to fan speed, fansequencing parameters, and other particular outputs by one or more othercontrollers, sequencers, or variable speed drives. Equations 1 and 2above are described in greater detail in Chapter 41 of the 2007 ASHRAEHandbook of HVAC Applications at pages 41.12-41.15. Equations 1 and 2are examples of open-loop or mode-based control strategies that may beswitched to after learning parameters using an extremum seeking controlstrategy.

Referring now to FIG. 5B, an illustration of the relationship betweenthe cooling tower fan power and the corresponding chiller's power isshown. As airflow increases, fan power increases but there is areduction in the chiller power consumption due to a decreasingtemperature of the chilled water provided to the condenser of thechiller. As is illustrated in FIG. 5B, a minimum (i.e., optimal) totalpower can be obtained by finding the right chiller power consumption andtower fan power consumption. U.S. application Ser. No. 12/777,097describes systems and methods for using self-optimizing control to findthe minimum total power in a system represented by FIG. 5B.

Equation 1 above for G_(twr) is an open loop model where PLR_(twr,cap)is one of the inputs that drives tower airflow (G_(twr)). PLR_(twr,cap)may be adjusted to change G_(twr) for a given PLR. Such an activitywould move the airflow and therefore the total energy plotted in FIG.5B.

The following procedure can be used to automatically determine an openloop parameter or parameters (e.g., PLR_(twr,cap) and/or β_(twr)) foruse in equation 1 or equation 2 listed above. Extremum seeking controlmay be used to control the cooling tower fans for an initial trainingperiod (e.g., 4 weeks, 1 week, etc.). During the training period, thepart load ratio (PLR) and tower airflow divided by maximum airflow withall tower cells operating at high speed (G_(twr)) can be recorded (e.g.,every hour, every thirty minutes, etc.). The maximum part load ratio(PLR_(max)) and minimum part load ratio (PLR_(min)) may be found andstored for the training period.

Using the training data, parameters PLR_(twr,cap) and β_(twr) may beidentified using a measurement, calculation, a least squares method, oranother approach. For example, if a linear line does not fit thetraining data, then an alternative equation (e.g., quadratic) may beused to determine the relationship between the part load ratio andrelative total airflow. Other methods such as artificial neural networksor non-parametric curve estimation methods could be used to estimate theoptimal tower airflow from the part-load ratio. Other descriptors suchas standard deviation of the errors from the curve fit may be calculatedand stored.

Once the relationship between optimal tower airflow and optimal chilledwater load (e.g., in terms of PLR_(twr,cap) and/or β_(twr)) aredetermined, control of the cooling tower can be switched from theextremum seeking controller to an open loop model-based controlleroperating according to equations 1 and equation 2. PLR_(twr,cap) andβ_(twr) learned during the extremum seeking control are used inequations 1 and 2. PLR_(twr,cap) and β_(twr) may be considered to be theresult of learning a relationship between a manipulated variable (e.g.,tower airflow) and an output variable (e.g., relative chilled waterload) when the relative cooling tower airflow is at a value thatminimizes the total power of the chiller and fan.

When control switches from the extremum seeking controller to equations1 and 2, and the PLR is between PLR_(min) and PLR_(max), then equations1 and 2 (e.g., a model-based controller implementing equations 1 and 2)continue controlling the cooling tower in a open loop control. If thePLR is less than PLR_(min) or greater than PLR_(max), then the trainingperiod may be repeated and the optimal relationship between towerairflow and power may be relearned, with the relationship beingcommunicated to the mode-based controller at the end of the extremumcontrol using updated values for PLR_(twr,cap),β_(twr), PLR_(min), andPLR_(max).

Due to equipment wear, system faults, or improper maintenance, theperformance of the cooling towers and chillers may be time varying.Periodically (once per week), the extremum seeking control (e.g., ashortened version of the training activity) may be used to determine anew data set for the relative chilled water load (e.g., part loadratio).

In another embodiment, whether extremum seeking control for training isdesired can be determined by a main control module by estimating the PLRfrom equation 1. If the actual optimal air flow ratio is significantlydifferent than the estimated optimal airflow ratio, then the performanceof the chiller and the cooling tower can be estimated to have changed.In an embodiment, the standard deviation of the residuals from the leastsquares method can be used to determine if there is a significantdifference between the estimated and actual optimal airflow ratio. Theoperator should be informed of the change and extremum seeking can bereinitiated to retain the system.

Referring now to FIG. 5C, a block diagram of an HVAC system 500 isshown, according to an exemplary embodiment. The HVAC system 500 isshown to include a controller 540 that is generally configured toprovide an airflow command to cooling tower 501. Controller 540 includesa model-based controller 542 and a self-optimizing controller 544. In anexemplary embodiment, model-based controller 542 is configured tooperate the open-loop equations 1 and 2 listed above. Self-optimizingcontroller 544 may be configured to use extremum seeking control to seeka total airflow parameter that results in a minimum tower fan power pluschiller power expenditure.

Self-optimizing controller 544 may be used to find parametersPLR_(twr,cap) and β_(twr) associated with a near optimal relationshipbetween airflow (i.e., the manipulated variable) and total power (i.e.,the output variable). Parameters PLR_(twr,cap) and β_(twr) can beprovided to the model-based controller (e.g., at the end of a trainingperiod) as described above.

Self-optimizing controller 544 is configured to control the speed of fan502 by providing a control signal to fan motor 503 or to a variablespeed drive 504 associated with fan motor 503. Throughout thisdisclosure, any reference to controlling the speed of the cooling towerfan can be or include controlling the speed of the fan motor, providingan appropriate control signal to the fan motor's variable speed drive,or any other control activity that affects the cooling tower fan speedof cooling tower system 505.

Self-optimizing controller 544 determines the fan speed by searching fora fan speed (e.g., an optimum fan speed) that minimizes the sum of thepower expended by cooling tower fan system 505 and the power expended bychiller 514 (e.g., power expended by the chiller's compressor). Thepower demand of the chiller's compressor (and/or other components) isaffected by the condenser water supply temperature—the temperature ofthe water supplied by cooling tower 501 to chiller 514. Increasing theair flow of the cooling tower 501 (e.g., increasing the fan speed)provides a lower condenser water temperature, which reduces thechiller's power requirement (primarily the power expended by thechiller's compressor). Increasing the fan speed, however, causes anincrease in tower fan power consumption. As shown in FIG. 5B, there isan optimal cooling tower air flow rate that minimizes the sum of theexpended chiller power and the power expended by the cooling tower fansystem.

Self-optimizing controller 544 receives an input 517 of power expendedby cooling tower fan system 505 and an input 515 of power expended bychiller 514 (e.g., chiller 514's compressor). Self-optimizing controller544 implements an extremum seeking control strategy that dynamicallysearches for an unknown input (e.g., optimal tower fan speed) to obtainsystem performance (e.g., power expended by the cooling tower and thechiller) that trends near optimal. Self-optimizing controller 544operates by obtaining a gradient of process system output (e.g., powerexpended by the cooling tower and the chiller) with respect to processsystem input (fan speed) by slightly perturbing or modulating the fanspeed and applying a demodulation measure to the output. Self-optimizingcontroller 544 provides control of the process system (e.g., the fanspeed and therefore the tower and chiller power demand) by driving theobtained gradient toward zero using an integrator or another mechanismfor reducing a gradient in a closed-loop system.

Inputs 506 and 515 may be summed outside of self-optimizing controller544 via summing block 516 to provide combined signal 517 (e.g., whichmay be representative of total power demand of tower fan system 505 andchiller 514). In various other embodiments, self-optimizing controller544 conducts the summation of summing block 516. In either case,self-optimizing controller 544 can be said to receive inputs 506 and 515(even if inputs 506 and 515 are provided as a single summed or combinedsignal 517).

Chiller 514 is shown as a simplified block diagram. Particularly,chiller is shown to include a condenser, an evaporator, a refrigerantloop, and a compressor. Chiller 514 also includes at least one expansionvalve on its refrigerant loop between the condenser and the evaporator.Chiller 514 can also include any number of sensors, control valves, andother components that assist the refrigeration cycle operation ofchiller 514.

A chilled fluid pump 520 pumps the chilled fluid through the loop thatruns through the building (e.g., through piping 522 and 524, throughchiller 514, and to one or more air handling units 526). In theembodiment shown in FIG. 5C, the chilled fluid is supplied via piping522 to an air handling unit 526 that is an economizer type air handlingunit. Economizer type air handling units vary the amount of outdoor airand return air used by the air handling unit for cooling. Air handlingunit 526 is shown to include economizer controller 528 that utilizes oneor more algorithms (e.g., state based algorithms, extremum seekingcontrol algorithms, etc.) to affect the actuators and dampers or fans ofair handling unit 526. The flow of chilled fluid supplied to airhandling unit 526 can also be variably controlled and is shown in FIG.5C as being controlled by proportional-integral (PI) control 530. PIcontrol 530 can control the chilled fluid flow to air handling unit 526to achieve a setpoint supply air temperature. Economizer controller 528,a controller for chiller 514, and PI control 530 can be supervised byone or more building management system (BMS) controllers 532. BMScontroller 532 can use BMS sensors 534 (connected to BMS controller 532via a wired or wireless BMS or IT network) to determine if the setpointtemperatures for the building space are being achieved. BMS controller532 can use such determinations to provide commands to PI control 530,chiller 514, economizer controller 528, or other components of thebuilding's HVAC system.

In an exemplary embodiment, self-optimizing controller 544 does notreceive control commands from BMS controller 532 or does not base itsoutput calculations on an input from BMS controller 532. In otherexemplary embodiments self-optimizing controller 544 receivesinformation (e.g., commands, setpoints, operating boundaries, etc.) fromBMS controller 532. For example, BMS controller 532 may provideself-optimizing controller 544 with a high fan speed limit and a low fanspeed limit. A low limit may avoid frequent component and power taxingfan start-ups while a high limit may avoid operation near the mechanicalor thermal limits of the fan system.

While controller 540 is shown as separate from BMS controller 532,controller 540 may be integrated with BMS controller 532. For example,controller 540 may be a software module configured for execution by aprocessor of BMS controller 532. In such an embodiment, the inputs ofexpended chiller power 515 and tower system fan power 506 may besoftware inputs. For example, software executed by BMS controller 532may use model-based calculations to determine the expended power. Themodels may relate, for example, fan speed to power expended by coolingtower fan system 505 and, for example, compressor pump speed to powerexpended by chiller 514. In yet other exemplary embodiments the inputsof expended power may be “real” (e.g., a current sensor coupled to thepower input of variable speed drive 504 of cooling tower fan system 505may be wired to an input of controller 540 or self-optimizing controller544, summing element 516, or BMS controller 532, and a current sensorcoupled to the power input of the variable speed compressor motor may bewired to another input of controller 540, summing element 516, or BMScontroller 532).

Where air handling unit 526 is an economizer, one or more controllers asdescribed herein may be used to provide for control of air handling unit526 during one or more of the operational states of the economizer. Forexample, economizer controller 528 may include an extremum seekingcontroller or control module configured to utilize an extremum seekingcontrol strategy to change the position of one or more outdoor airactuators or dampers. One or more of the systems and methods describedwith reference to FIGS. 1-4 may be implemented in or by economizercontroller 528.

Other exemplary embodiments may include a configuration different thanthat shown in FIG. 5C. In such different configurations, for example,additional or different inputs or outputs may be used by controller 540.For example, controller 540 may manipulate one or more variables ofchiller 514 or pump 518 alone or in concert with one or more manipulatedvariables for cooling tower system 505. Power expenditures of chiller514, pump 518, cooling tower system 505 and/or other cooling systemoutputs may all be summed at element 516. One or more of the manipulatedvariables may be adjusted by self-optimizing controller 544 to seek anoptimal aggregate power expenditure. Accordingly, a single ormulti-variable steady state relationship may be learned byself-optimizing controller 544 and used by model-based controller 542after a switch from the self-optimizing controller to the model-basedcontroller 542 occurs.

The construction and arrangement of the systems and methods as shown inthe various exemplary embodiments are illustrative only. Although only afew embodiments have been described in detail in this disclosure, manymodifications are possible (e.g., variations in sizes, dimensions,structures, shapes and proportions of the various elements, values ofparameters, mounting arrangements, use of materials, orientations,etc.). For example, the position of elements may be reversed orotherwise varied and the nature or number of discrete elements orpositions may be altered or varied. Accordingly, all such modificationsare intended to be included within the scope of the present disclosure.The order or sequence of any process or method steps may be varied orre-sequenced according to alternative embodiments. Other substitutions,modifications, changes, and omissions may be made in the design,operating conditions and arrangement of the exemplary embodimentswithout departing from the scope of the present disclosure.

The present disclosure contemplates methods, systems and programproducts on any machine-readable media for accomplishing variousoperations. The embodiments of the present disclosure may be implementedusing existing computer processors, or by a special purpose computerprocessor for an appropriate system, incorporated for this or anotherpurpose, or by a hardwired system. Embodiments within the scope of thepresent disclosure include program products comprising machine-readablemedia for carrying or having machine-executable instructions or datastructures stored thereon. Such machine-readable media can be anyavailable media that can be accessed by a general purpose or specialpurpose computer or other machine with a processor. By way of example,such machine-readable media can comprise RAM, ROM, EPROM, EEPROM, CD-ROMor other optical disk storage, magnetic disk storage or other magneticstorage devices, or any other medium which can be used to carry or storedesired program code in the form of machine-executable instructions ordata structures and which can be accessed by a general purpose orspecial purpose computer or other machine with a processor. Combinationsof the above are also included within the scope of machine-readablemedia. Machine-executable instructions include, for example,instructions and data which cause a general purpose computer, specialpurpose computer, or special purpose processing machines to perform acertain function or group of functions.

Although the figures may show a specific order of method steps, theorder of the steps may differ from what is depicted. Also two or moresteps may be performed concurrently or with partial concurrence. Suchvariation will depend on the software and hardware systems chosen and ondesigner choice. All such variations are within the scope of thedisclosure. Likewise, software implementations could be accomplishedwith standard programming techniques with rule based logic and otherlogic to accomplish the various connection steps, processing steps,comparison steps and decision steps.

1. A system for operating a process, comprising: a processing circuitthat uses a self-optimizing control strategy to learn a steady staterelationship between a manipulated variable and an output variable;wherein the processing circuit is configured to switch from using theself-optimizing control strategy to using a second control strategy thatoperates based on the learned steady state relationship.
 2. The systemof claim 1, wherein the second control strategy is an open loop controlstrategy that conducts open loop control based on the learnedsteady-state relationship.
 3. The system of claim 1, wherein the secondcontrol strategy includes a feedback loop that operates based on thelearned steady-state relationship.
 4. The system of claim 1, wherein theprocessing circuit is configured to operate in the second controlstrategy normally and the self-optimizing control strategy periodically.5. The system of claim 1, wherein the processing circuit is configuredto operate the process according to the self-optimizing control strategyduring a start-up state of the process.
 6. The system of claim 5,wherein the processing circuit is configured to detect when a steadystate has been reached after the start-up of the process.
 7. The systemof claim 6, wherein learning a steady-state relationship between theinput and output comprises seeking a manipulated variable estimated tominimize energy consumption.
 8. The system of claim 7, wherein theself-optimizing control strategy is an extremum seeking controlstrategy.
 9. The system of claim 1, wherein the self-optimizing controlstrategy is an extremum seeking control strategy.
 10. A method foroperating a process, comprising: using a self-optimizing controller tolearn a steady state relationship between a manipulated variable and anoutput variable; and switching from using the self-optimizing controllerto using a second controller that operates based on the learned steadystate relationship.
 11. The method of claim 10, wherein the secondcontroller uses an open loop control strategy that conducts open loopcontrol based on control variables observed in the learned steady staterelationship.
 12. The method of claim 10, further comprising: operatingthe process using the second controller primarily and operating usingthe self-optimizing controller periodically.
 13. The method of claim 10,further comprising: operating the process using the self-optimizingcontroller during at least one of a start-up state and a training stateof the process.
 14. The method of claim 10, further comprising:detecting whether a steady state has been obtained and learning thesteady state relationship between a manipulated variable and an outputvariable by recording calculated and/or sensed parameters existingduring the steady state relationship, the calculated and/or sensedparameters provided to the second controller for operation of amodel-based control strategy.
 15. The method of claim 10, wherein theself-optimizing controller is an extremum seeking controller.
 16. Asystem for controlling a cooling tower that cools condenser fluid for acondenser of a chiller, comprising: a cooling tower fan system thatcontrollably varies a speed of at least one fan motor; an extremumseeking controller that receives inputs of power expended by the coolingtower fan system and of power expended by the chiller, wherein theextremum seeking controller provides an output to the cooling tower fansystem that controls the speed of the at least one fan motor, whereinthe extremum seeking controller determines the output by searching for aspeed of the at least one fan motor that minimizes the sum of the powerexpended by the cooling tower fan system and the power expended by thechiller; a model-based controller for controlling the speed of the atleast one fan motor; a processing circuit configured to store processcharacteristics associated with a steady-state of the extremum seekingcontroller; wherein the processing circuit is further configured toswitch from using the extremum seeking controller to using themodel-based controller to control the fan speed and wherein theprocessing circuit operates the model-based controller using the storedprocess characteristics.
 17. The system of claim 16, wherein theprocessing circuit is configured to use the extremum seeking controllerduring an initial training period.
 18. The system of claim 17, whereinthe stored process characteristics associated with the steady state ofthe extremum seeking controller and used by the model-based controllercomprise the part-load ratio at which the cooling tower operates at itscapacity and the slope of the relative cooling tower airflow versus thepart-load ratio.
 19. The system of claim 18, wherein the processingcircuit is configured to store the maximum part load ratio (PLR_(max))during the training period and the minimum part load ratio (PLR_(min))during the training period.
 20. The system of claim 19, wherein theprocessing circuit is configured to monitor the part load ratio duringthe model-based control and wherein the processing circuit is configuredto switch back to using the extremum seeking controller if the part loadratio exceeds PLR_(max) or drops below PLR_(min) during operation usingthe model-based controller.